The rising sale of 3D-enabled TVs and personal devices in the consumer segment, releasing of new and old movies in 3D and increasing use of large screen electronic billboards which can display attention grabbing 3D-images for advertising or informational purposes, has increased the need for creating 3D-content. The ability to convert existing 2D content to 3D content automatically or with limited manual intervention can result in large cost and time saving and will grow the 3D-content creation market even further.
Traditionally, converting 2D videos to 3D for professional application consists of very labor intensive process of roto-scoping where objects in each frame are manually and painstakingly traced by the artist and depth information for each object is painted by hand. This traditional 2D to 3D conversion suffers from disadvantages. Depending on the complexity of the scene in each frame, it may take several hours to several days to generate a depth map of a single frame. A 2-hour movie at 24 frames per second may contain up to one hundred thousand unique frames and this manual depth map creation can cost upwards of $200 per frame. Consequently, this method is very expensive and slow.
On the low end of the 2D to 3D conversion, consumer 3D-TV sets have built in hardware that can automatically convert 2D video or image into 3D in real time. However, the 3D quality is extremely poor with hardly any depth effect in the converted 3D-image. Such fully automated method is obviously not acceptable by professional movie post-production houses.
There have been numerous research publications on methods of automatically generating depth map from a mono-ocular 2D-image for the purpose of converting the 2D-image to 3D-image. The methods range from very simplistic heuristics to very complicated and compute intensive image analysis. Simple heuristics may be suitable for real time conversion application but provides poor 3D quality. On the other hand, complex mathematical analysis may provide good 3D-image quality but may not be suitable for real time application and hardware implementation.
A solution to this quality versus difficulty dilemma is to start with an automated default lower quality 3D-image and provide ability to add additional manual editing capabilities to enhance the 3D image quality.